Effects of anoxic prognostic model on immune microenvironment in pancreatic cancer

Pancreatic cancer has one of the worst prognoses in the world, which suggests that the tumor microenvironment, which is characterized by hypoxia and immunosuppression, plays a significant role in the prognosis and progression of pancreatic cancer. We identified PLAU, LDHA, and PKM as key genes involved in pancreatic cancer hypoxia through GO/KEGG enrichment related hypoxia pathways and cox regression, established prognostic models, and studied their relationship to immune invasion through bioinformatics using R and related online databases. We verified the high expression of PLAU, LDHA, and PKM in pancreatic cancer cells using qPCR in vitro, and we also discovered that the expression of PLAU, LDHA, and PKM in hypoxic pancreatic cancer cells differed from that in normal cultured pancreatic cancer cells. Finally, we discovered that our prognostic model accurately predicted postrain in pancreatic cancer patients with hypoxia and immune infiltration.

The prognosis for pancreatic cancer is among the worst in the world, and it is the fourth leading cause of cancer-related death worldwide 1 .The specific position of the pancreas in the abdominal cavity causes concealed characteristics of pancreatic cancer in its early stages 2 , and the absence of extremely sensitive molecularly targeted markers for pancreatic cancer makes early diagnosis difficult 3,4 . Pancreatic cancer is highly aggressive 5 . Once diagnosed, it is likely to be advanced, making it difficult to find an effective treatment plan 5 . Although pancreatic cancer has been extensively studied in recent years and research results on its diagnosis, radiotherapy technology, and systematic treatment have been continuously proposed, the survival rate of the disease has not improved significantly, and the number of deaths related to the disease continues to rise 6 .
In pancreatic cancer, malignant cells account for only a small proportion; the remainder is composed primarily of fibroblasts, extracellular matrix, endothelial cells, and hematopoietic cells, and these host components constitute the tumor microenvironment 7 . The biological function of tumors is mainly determined by the interaction between cancer cells and their microenvironment 7,8 . These tumor microenvironment cell types contribute to a highly immunosuppressive, hypoxic, and pro-fibroproliferative cancer 7,9 . Hypoxia is one of the significant features of the pancreatic tumor microenvironment, which is due to a wide range of connective tissue hyperplasia and secondary vascular decrease 10 . Hypoxia is also one of the factors that contribute to the progression of pancreatic cancer 11 , and it has played a key role in a variety of cells and biological events, including cell proliferation, survival, angiogenesis, metabolism, tumor growth, invasion, and metastasis 12 . Hypoxia is one of the important microenvironmental characteristics of pancreatic cancer. Hypoxia microenvironment can induce HIF-1α factor list target to regulate downstream genes and promote downstream pathway activation.
Pancreatic cancer (PAAD) immune environment is generally considered immune suppression 13,14 . This immunosuppression is associated with poor prognosis with pancreatic cancer 15,16 , and the immune microenvironment of the tumor is very insensitive to immunotherapy, which causes a poor prognosis for pancreatic cancer 17 .
In conclusion, hypoxia and high immunosuppression in the microenvironment of pancreatic tumors play a significant role in the progression and prognosis of the tumor, and the development of relevant prognostic models is of great significance for the prognostic guidance of PAAD. At present, hypoxia is an essential factor in the pancreatic cancer microenvironment, and a model for predicting the prognosis of PAAD patients is an imperative necessity. The purpose of this study was to investigate special target molecules related to hypoxia in pancreatic cancer using bioinformatics methods, to investigate their role in the anoxic tumor microenvironment of pancreatic cancer, to establish related prognostic models, and to examine the important role of models in the immune microenvironment so as to provide new ideas for the prognosis and treatment of pancreatic cancer.

Validation of prognostic models.
To verify the accuracy of the established prognostic model, we used GSE85916 and ICGC-PACA-AU as the verification set, collected data from 174 GSE85916 and ICGC-PACA-AU pancreatic cancer patients (79 were from GSE85916 and 95 from ICGC-PACA-AU), and calculated risk scores using the same formula as in the training set. We divided the verification set into a high-risk group (n = 87) and a low-risk group (n = 87) based on the median risk score (Fig. 4A). As with the training set, we observed shorter survival times in the peak risk group (Fig. 4B). Time-dependent ROC analysis revealed that 1-year OS prognostic accuracy was 0.681 (95% CI 0.629-0.863), and 3-year OS was 0.649 (95% CI 0.690-0.940). The 5-year prognostic accuracy of OS was 0.758 (95% CI 0.745-1.007) (Fig. 4C).
Moreover, we constructed a calibration curve, which showed that our model was in good agreement with the actual survival of PAAD patients (Fig. 5A). In addition to establishing a nomogram of risk scores and traditional prognostic factors (Fig. 5C), we discovered that our model had a higher AUC value than traditional clinical factors (Fig. 5B).
Furthermore, we conducted multivariate COX regression analyses on risk scores and clinical factors that may affect the prognosis of PAAD patients, such as T stage, gender, age, and histological grade, and the results indicated that the risk scores of our prognostic model could be used as independent risk factors in multivariate COX (Supplementary file 2: Table S2). Immune cell infiltration level analysis. It was determined that neutrophils, Th1 cells, macrophages, and Th2 cells had a high degree of invasion, while PDC and Th17 cells had a low degree of invasion (Fig. 7A). Similarly, neutrophils, Th1 cells, macrophages, and Th2 cells were positively correlated with their risk scores, whereas PDC and Th17 cells were negatively correlated with their risk factors ( Fig. 7B-G).

Discussion
Pancreatic cancer is one of the most deadly malignancies 18 . While survival rates for other major cancers have improved substantially, but pancreatic cancer survival rates have not improved 19 . Pancreatic cancer is usually detected at an advanced stage, and most treatment options are ineffective, resulting in a poor prognosis 5 . The need for accurate prognostic information is not only for patients but also for clinicians to choose active treatment interventions and anticipate significant clinical benefit. Therefore, a reliable prognostic model is required to predict the prognosis of pancreatic cancer patients. Normal oxygen concentrations are necessary for the functioning and maintenance of aerobic organs 20 . Normal oxygen concentration is required for the normal functioning and maintenance of aerobic organs within an organization 21 . However, due to the uncontrolled growth and proliferation of tumor cells and abnormal tumor blood vessels, a large amount of nutrients and oxygen are consumed in tumor tissues, resulting in hypoxia 22 . In tumor tissues, the vascular network cannot form efficiently and promptly, and the neovascularization network has structural and functional abnormalities 23 . The aforementioned large number of neovascularization's of nonfunctional or functionally impaired blood vessels is another significant cause of hypoxia 24 . The hypoxia induced www.nature.com/scientificreports/ factor (HIF) transcription factor family and its downstream related signaling pathways are primarily responsible for regulating the adaptive process that occurs in tumor cells under hypoxic conditions 25 .
With the development of research in recent years, the study of PAAD biomarkers, prognostic markers, and prognostic models has received a growing amount of attention. Through enrichment analysis of differential genes from GEPIA2, we identified hypoxia-related genes, and through the PPI network, we determined the gene directly related to HIF1A, which plays the most critical role in the process of tumor hypoxia. We selected hypoxia-related prognostic genes (PLAU, PKM, and LDHA) using single-factor Cox regression screening and established prognostic models using Lasso analysis. After validation and analysis, we discovered that our model can accurately predict the prognosis of patients with pancreatic cancer. The predictive efficacy of this model (area under the ROC curve) was greater than that of traditional clinical prognostic factors, according to our study. We also revealed that our prognostic model was strongly correlated with clinical features, with higher risk scores in T and N stages and tumor residual indicating relatively malignant stages and excessive tumor residual. This indicates that our model is helpful for clinicians to improve their prognostic judgment of pancreatic cancer patients in order to complete the treatment of pancreatic cancer more effectively and promptly and to provide a selection of prognostic interventions, which is anticipated to significantly improve patient survival and prognostic effects.
In addition to improving oxygen delivery by promoting angiogenesis and erythropoiesis, HIF-1 also adapts to anoxic environments by regulating metabolism to reduce the need for oxygen in cells.In glucose metabolism, HIF-1 converts oxidative metabolism to glycolysis by promoting the expression of glycolytic enzymes, reducing the need for oxygen 26 . PKM is one of the key enzymes in the conversion of glucose to pyruvate, and LDHA can catalyze the conversion of pyruvate to lactic acid 27,28 . These genes were found to contain sequences similar to HIF-1 binding sites in erythropoietin enhancers 29 . Under hypoxia conditions, HIF-1 can transform www.nature.com/scientificreports/ oxidative metabolism to glycolysis and enhance glycolysis by regulating the expression of the above genes, so as to reduce oxygen demand and maintain normal energy supply, while inhibiting the cell damage caused by hypoxia-induced reactive oxygen species generation 30 . PLAU encodes a secretory serine protease that converts plasminogen to plasminase 31 . PLAU has also been found to be involved in a variety of cancers and is associated with poor prognosis in a variety of cancers 32 ; Some experimental studies have proved that PLAU overexpression is associated with poor prognosis of PAAD, and plays an important role in PAAD resistance, invasion and migration 33 . However, the role of PLAU in the anoxia process of pancreatic cancer has not been well understood, and our study aims to fill this gap. Tumor progression and prognosis are closely related to immune cell infiltration in the tumor microenvironment 17 . We found that macrophages, neutrophils, Th1 cells, Th2 cells, and macrophages were more aggressive in the high-risk group, whereas PDC and Th17 cells were less aggressive. The tumor immune microenvironment in rapidly progressing PAAD patients is often associated with inadequate infiltration of immune cells 34 . Macrophages and neutrophils promote the suppression of immunosuppressive cells 35 . In our study, increased infiltration of these two types of cells in patients at high risk of hypoxia can increase the immunosuppressive tumor microenvironment and promote the progression of pancreatic cancer, which is associated with a poor prognosis. An immune checkpoint is a series of molecules expressed on immune cells that regulate immune activation 36 . Immune checkpoints play a crucial role in carcinogenesis by promoting the www.nature.com/scientificreports/ immunosuppressive effect of tumors 37 . Tumors can protect themselves from attack by stimulating immune checkpoint targets 38 . The immune checkpoints PD-L1, PD-L2, and CD276 were also upregulated in the hyperhypoxic risk group in our study. Our model began with hypoxic-related genes in pancreatic cancer and simultaneously examined the relationship between the model and the immune microenvironment in order to examine the associated immune infiltration in high and low risk patient groups. We determined that our hypoxic-related gene model is highly correlated with the immune microenvironment, providing a novel method for predicting the prognosis of pancreatic cancer based on the tumor microenvironment. Immune checkpoints are a series of molecules expressed on immune cells that regulate the degree of immune activation and play an important role in preventing autoimmunity (when the immune function goes wrong and attacks healthy cells). In addition, we verified the expression difference of the three genes in pancreatic cancer cells and normal pancreatic ductal epithelial cells by RT-qPCR experiments, and also confirmed that the expression of the three genes differed in pancreatic cancer cells before and after hypoxia, indicating that these three genes have the potential to become hypoxia-related prognostic markers of pancreatic cancer.
Our model has the potential to be a prognostic model related to the tumor microenvironment of pancreatic cancer and can reliably predict the prognosis of patients based on the impact of the tumor microenvironment on pancreatic cancer.

Methods and materials
Cell culture and reagents. Human pancreatic cancer cell PANC-1 were purchased from PROCELL and human normal pancreatic ductal cell hTERT-HPNE were purchased from CELL RESEARCH. Both kinds of cells were stored in the sample bank of the Affiliated Hospital of Qingdao University by liquid nitrogen.Cells were cultured in DMEM high glucose medium supplemented with 10% fetal bovine serum and 1% penicillin/ streptomycin (purchased by Meilunbio) in a wet incubator with 5% carbon dioxide at 37 °C. Hypoxia-treated PANC-1 cells were cultured for 24 h in an anoxic incubator (Ruskinn Invivo2 400 Hypoxia workstation).

Search for genes in hypoxia.
We used the online database GEPIA2 39 to identify the differentially expressed genes of pancreatic cancer and took log2FoldChange (logFC) and P-values as the screening conditions for differentially expressed genes. We filter for P-value < 0.05 and | log2FC |> 1 gene as a difference between 2 conditions: log2FC > 2.5 for up-regulated, log2FC < − 2.5 for down-regulated. Afterwards, volcano maps were used to visualize these differential genes.  Table S3).
Previous research indicated that HIF1A plays a crucial role in hypoxia 40 . We mapped the PPI network of hypoxia-related genes by string, and looked for genes directly associated with HIF1A as key genes.
Single-factor COX regression was performed for the above key genes using the "survival" package. In p < 0.05 genes, the two genes with the lowest p value and the gene with the closest relationship with HIF1A (with the largest combined score) were selected as the genes to establish the prognostic model. The prognostic model was established using LASSO regression 41 . Construction of a prognostic model. Risk scores were calculated based on standardized PAAD mRNA expression data in TCGA.
In this study, the R software package glmnet was used to integrate survival time, survival state, and gene expression data, and regression analysis was performed using the lasso-cox method. In addition, we also  www.nature.com/scientificreports/ implemented a tenfold cross-validation procedure to determine the optimal model. PAAD patients were divided into high-risk and low-risk groups based on the median risk score for OS survival analysis. An ROC curve was developed using the "timeROC" package to evaluate the prognostic effect of the model. To verify the accuracy of the established prognostic model, we used   GSE85916 and ICGC-PACA-AU as the verification set(Supplementary file 4: Table S4). Meanwhile, we use the "rms" and "survival" packages to evaluate the effect of the model's prediction on the actual outcome by plotting the actual probability and the probability predicted by the model in different situations in the graph.

Validation of prognostic models.
The "pROC" package was then used to plot ROC curves for risk score and associated clinical variables in order to determine whether our model has higher predictive power for prognosis compared with traditional clinical prognosis scoring systems.
Multivariate Cox regression analyses were performed for clinicopathologic parameters such as histological grade and T stage to evaluate whether the risk scoring system could be used as an independent predictor. We also analyzed the relationship between risk score and the clinical characteristics of TCGA-PAAD cohort patients.

Immune cell infiltration level analysis.
On the basis of the ssGSEA algorithm provided by the R-package GSVA, the immune infiltration corresponding to the prognostic model was calculated using 24 types of immune cell markers from the Immunity article 21 .
Concurrently, the expression of immune checkpoint related molecules in patients with pancreatic cancer was determined.
Real-time quantitative PCR. Total RNA was extracted from PANC-1, hTERT-HPNE, and PANC-1 cultured for 24 h after hypoxia using the anaerobic incubator. Reversely transcribed was performed using the PrimeScrip RT-PCR kit (Takara, Japan), and RT-qPCR was performed on a Roche instrument with SYBR PreMix Ex Taq (Takara, Japan). Primer sequences used in this study are shown as follows: were used for statistical analysis. The Student t-test was used to analyze the difference in genes. |logFC|> 1 and P < 0.05 were set as thresholds to choose the significance of the differential expression gene. Univariate and multivariate COX analysis, the log-rank test, and logistic regression analysis were employed.

Data availability
The datasets used and/or analyzed during this study are available upon reasonable request from the corresponding author.